The goal of this project is to determine whether three complementary biomarkers of effect can be used to detect and quantify the somatic effects of occupational exposure to pesticides. The biomarkers are: fluorescence in situ hybridization (FISH) to identify specific chromosome rearrangements, glycophorin A analysis (GPA) to enumerate variant erythrocytes, and the polymerase chain reaction(PCR) to detect T-cell receptor V(D)J rearrangements, Two exposure groups will be examined: phosphine fumigators, and herbicide/insecticide applicators. These groups were selected because previous reports have indicated that they are at increased risk of developing non-Hodgkin's lymphoma, and because they have elevated levels of genetic damage in somatic cells. G-banding data indicate that phosphine fumigators have increased chromosome breakage in bands 1p13, 2p23, 14q32, and 21q22 which correspond to oncogenes NRAS, NMYC, ELK2 and ETS-2, respectively. GPA variant erythrocyte frequencies appear to be elevated in the herbicide/insecticide group, and PCR analysis of fumigators suggests increased frequencies of V(D)J gene rearrangements on chromosome 7. Analysis of these exposed populations will enable us to determine whether FISH, GPA, and PCR might be useful for complementing surveillance data to define persons at increased risk of disease following pesticide exposure. The results will be compared to a control group consisting of state grain inspectors which match the general socio- economic and lifestyle characteristics of the exposure groups. Controls will be matched to cases by county of residence to control for lifestyle factors, and will be drawn from the same age and tobacco usage strata. This design was selected to maximize the power of detecting case-control differences in the endpoint biomarker assays by matching for the known and suspected confounders. By selecting subjects at the extremes of the age and tobacco usage spectra, the design also maximizes the precision for measuring the slopes of the age-response and the smoking-response regressions as well as the (possible) differences in regression slopes between the exposed and unexposed groups.